Communications networks are currently undergoing a revolution brought about by the increasing demand for real-time information being delivered to a diversity of locations. Many situations require the ability to transfer large amounts of data across geographical boundaries with increasing speed and accuracy. However, with the increasing size and complexity of the data that is currently being transferred, maintaining the speed and accuracy is becoming increasingly difficult.
Early communications networks resembled a hierarchical star topology. All access from remote sites was channeled back to a central location where a mainframe computer resided. Thus, each transfer of data from one remote site to another, or from one remote site to the central location, had to be processed by the central location. This architecture is very processor-intensive and incurs higher bandwidth utilization for each transfer. This was not a major problem in the mid to late 1980s where fewer remote sites were coupled to the central location. Additionally, many of the remote sites were located in close proximity to the central location. Currently, hundreds of thousands of remote sites are positioned in various locations across assorted continents. Legacy networks of the past are currently unable to provide the data transfer speed and accuracy demanded in the marketplace of today.
In response to this exploding demand, data transfer through networks employing distributed processing has allowed larger packets of information to be accurately and quickly distributed across multiple geographic boundaries. Today, many communication sites have the intelligence and capability to communicate with many other sites, regardless of their location. This is typically accomplished on a peer level, rather than through a centralized topology, although a host computer at the central site can be appraised of what transactions take place and can maintain a database from which management reports are generated and operation issues addressed.
Distributed processing currently allows the centralized site to be relieved of many of the processor-intensive data transfer requirements of the past. This is typically accomplished using a data network, which includes a collection of routers. The routers allow intelligent passing of information and data files between remote sites. However, increased demand and the sophistication required to route current information and data files quickly challenged the capabilities of existing routers. Also, the size of the data being transmitted is dramatically increasing. Some efficiencies are obtained by splitting longer data files into a collection of smaller, somewhat standardized cells for transmission or routing. However, these efficiencies are somewhat offset by the processing required to process the cells at nodes within the network.
More specifically, within the system there are limitations associated with passing event signals between two different subsystems or within a subsystem that employs two clock zones having asynchronous clock rates. Currently, this typically requires a “four-edge” synchronization process between the two asynchronous clock zones. This four-edge synchronization process requires the generation of a first event signal in a first clock zone that is then recognized and acknowledged by a first event signal in the second clock zone. A second event signal is then generated in the first clock zone to acknowledge that the second clock zone has acknowledged the first event signal in the first clock zone. Then, a second event signal is generated in the second clock zone that acknowledges the second event signal acknowledgment in the first clock zone. This process is time consuming and slows the interchange of information or data within a system or subsystem.
Accordingly, what is needed in the art is an enhanced way to pass event signals between two asynchronous clock zones.